Boosting Mixture Models for Semi-supervised Learning View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2001

AUTHORS

Yves Grandvalet , Florence d’Alché-Buc , Christophe Ambroise

ABSTRACT

This paper introduces MixtBoost, a variant of AdaBoost dedicated to solve problems in which both labeled and unlabeled data are available. We propose several definitions of loss for unlabeled data, from which margins are defined. The resulting boosting schemes implement mixture models as base classifiers. Preliminary experiments are analyzed and the relevance of loss choices is discussed. MixtBoost improves on both mixture models and AdaBoost provided classes are structured, and is otherwise similar to AdaBoost. More... »

PAGES

41-48

References to SciGraph publications

  • 2001-03. Soft Margins for AdaBoost in MACHINE LEARNING
  • Book

    TITLE

    Artificial Neural Networks — ICANN 2001

    ISBN

    978-3-540-42486-4
    978-3-540-44668-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-44668-0_7

    DOI

    http://dx.doi.org/10.1007/3-540-44668-0_7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1037922808


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Statistics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Heuristics and Diagnostics for Complex Systems", 
              "id": "https://www.grid.ac/institutes/grid.462261.5", 
              "name": [
                "Heudiasyc, UMR CNRS 6599, Universit\u00e9 de Technologie de Compi\u00e8gne, BP 20.529, 60205\u00a0Compi\u00e8gne cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Grandvalet", 
            "givenName": "Yves", 
            "id": "sg:person.015255215731.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015255215731.52"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Laboratoire d'informatique de Paris 6", 
              "id": "https://www.grid.ac/institutes/grid.462751.3", 
              "name": [
                "LIP6, UMR CNRS 7606, Universit\u00e9 Pierre et Marie Curie, 4, place Jussieu, 75252\u00a0Paris Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "d\u2019Alch\u00e9-Buc", 
            "givenName": "Florence", 
            "id": "sg:person.0643253341.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643253341.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Heuristics and Diagnostics for Complex Systems", 
              "id": "https://www.grid.ac/institutes/grid.462261.5", 
              "name": [
                "Heudiasyc, UMR CNRS 6599, Universit\u00e9 de Technologie de Compi\u00e8gne, BP 20.529, 60205\u00a0Compi\u00e8gne cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ambroise", 
            "givenName": "Christophe", 
            "id": "sg:person.016650156731.69", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016650156731.69"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1023/a:1007618119488", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003090683", 
              "https://doi.org/10.1023/a:1007618119488"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jcss.1997.1504", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004338842"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aos/1016218223", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020629296"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aos/1024691352", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035391848"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2001", 
        "datePublishedReg": "2001-01-01", 
        "description": "This paper introduces MixtBoost, a variant of AdaBoost dedicated to solve problems in which both labeled and unlabeled data are available. We propose several definitions of loss for unlabeled data, from which margins are defined. The resulting boosting schemes implement mixture models as base classifiers. Preliminary experiments are analyzed and the relevance of loss choices is discussed. MixtBoost improves on both mixture models and AdaBoost provided classes are structured, and is otherwise similar to AdaBoost.", 
        "editor": [
          {
            "familyName": "Dorffner", 
            "givenName": "Georg", 
            "type": "Person"
          }, 
          {
            "familyName": "Bischof", 
            "givenName": "Horst", 
            "type": "Person"
          }, 
          {
            "familyName": "Hornik", 
            "givenName": "Kurt", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/3-540-44668-0_7", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": {
          "isbn": [
            "978-3-540-42486-4", 
            "978-3-540-44668-2"
          ], 
          "name": "Artificial Neural Networks \u2014 ICANN 2001", 
          "type": "Book"
        }, 
        "name": "Boosting Mixture Models for Semi-supervised Learning", 
        "pagination": "41-48", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/3-540-44668-0_7"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "b4a6ca3b9ed093c9f171502fb4eaf232ac61f7d396113bc9560f09c20c6bc1e7"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1037922808"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/3-540-44668-0_7", 
          "https://app.dimensions.ai/details/publication/pub.1037922808"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T14:26", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8669_00000266.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/3-540-44668-0_7"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/3-540-44668-0_7'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-44668-0_7'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-44668-0_7'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-44668-0_7'


     

    This table displays all metadata directly associated to this object as RDF triples.

    105 TRIPLES      23 PREDICATES      31 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/3-540-44668-0_7 schema:about anzsrc-for:01
    2 anzsrc-for:0104
    3 schema:author N0be32eff88c54c729d323ccf31b69b59
    4 schema:citation sg:pub.10.1023/a:1007618119488
    5 https://doi.org/10.1006/jcss.1997.1504
    6 https://doi.org/10.1214/aos/1016218223
    7 https://doi.org/10.1214/aos/1024691352
    8 schema:datePublished 2001
    9 schema:datePublishedReg 2001-01-01
    10 schema:description This paper introduces MixtBoost, a variant of AdaBoost dedicated to solve problems in which both labeled and unlabeled data are available. We propose several definitions of loss for unlabeled data, from which margins are defined. The resulting boosting schemes implement mixture models as base classifiers. Preliminary experiments are analyzed and the relevance of loss choices is discussed. MixtBoost improves on both mixture models and AdaBoost provided classes are structured, and is otherwise similar to AdaBoost.
    11 schema:editor N5b022e7ec69b4edfbc614679dac4f5a5
    12 schema:genre chapter
    13 schema:inLanguage en
    14 schema:isAccessibleForFree true
    15 schema:isPartOf Na0873fb25e1d4320a0eb2039393a23d2
    16 schema:name Boosting Mixture Models for Semi-supervised Learning
    17 schema:pagination 41-48
    18 schema:productId N31f1f490e6d7494e833f0e177096a91b
    19 Ne326708c458242aba78c398cc185b0ce
    20 Nfd0dcf6693ec4968a9dc1572247e5b69
    21 schema:publisher Ne17cef5d23fa45cbafb016e6313a1de2
    22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037922808
    23 https://doi.org/10.1007/3-540-44668-0_7
    24 schema:sdDatePublished 2019-04-15T14:26
    25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    26 schema:sdPublisher Nc246e2476d6b4048a01059f986ec8c69
    27 schema:url http://link.springer.com/10.1007/3-540-44668-0_7
    28 sgo:license sg:explorer/license/
    29 sgo:sdDataset chapters
    30 rdf:type schema:Chapter
    31 N01c4b323efac4d6d920841714a16584c rdf:first Nb38e497bc8a44fec9892bf48917d7261
    32 rdf:rest rdf:nil
    33 N0be32eff88c54c729d323ccf31b69b59 rdf:first sg:person.015255215731.52
    34 rdf:rest N8034d9651f6745bd995493ce09617466
    35 N31f1f490e6d7494e833f0e177096a91b schema:name readcube_id
    36 schema:value b4a6ca3b9ed093c9f171502fb4eaf232ac61f7d396113bc9560f09c20c6bc1e7
    37 rdf:type schema:PropertyValue
    38 N400ebfe037064d95a939b53a6e0fc7ef rdf:first sg:person.016650156731.69
    39 rdf:rest rdf:nil
    40 N5b022e7ec69b4edfbc614679dac4f5a5 rdf:first N73309b7246564eef83295d9b5b0faef1
    41 rdf:rest Nc03050a6258b488f9619094ec4234e9e
    42 N73309b7246564eef83295d9b5b0faef1 schema:familyName Dorffner
    43 schema:givenName Georg
    44 rdf:type schema:Person
    45 N7e0cd0dedab147988162b865c3449e3d schema:familyName Bischof
    46 schema:givenName Horst
    47 rdf:type schema:Person
    48 N8034d9651f6745bd995493ce09617466 rdf:first sg:person.0643253341.50
    49 rdf:rest N400ebfe037064d95a939b53a6e0fc7ef
    50 Na0873fb25e1d4320a0eb2039393a23d2 schema:isbn 978-3-540-42486-4
    51 978-3-540-44668-2
    52 schema:name Artificial Neural Networks — ICANN 2001
    53 rdf:type schema:Book
    54 Nb38e497bc8a44fec9892bf48917d7261 schema:familyName Hornik
    55 schema:givenName Kurt
    56 rdf:type schema:Person
    57 Nc03050a6258b488f9619094ec4234e9e rdf:first N7e0cd0dedab147988162b865c3449e3d
    58 rdf:rest N01c4b323efac4d6d920841714a16584c
    59 Nc246e2476d6b4048a01059f986ec8c69 schema:name Springer Nature - SN SciGraph project
    60 rdf:type schema:Organization
    61 Ne17cef5d23fa45cbafb016e6313a1de2 schema:location Berlin, Heidelberg
    62 schema:name Springer Berlin Heidelberg
    63 rdf:type schema:Organisation
    64 Ne326708c458242aba78c398cc185b0ce schema:name doi
    65 schema:value 10.1007/3-540-44668-0_7
    66 rdf:type schema:PropertyValue
    67 Nfd0dcf6693ec4968a9dc1572247e5b69 schema:name dimensions_id
    68 schema:value pub.1037922808
    69 rdf:type schema:PropertyValue
    70 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    71 schema:name Mathematical Sciences
    72 rdf:type schema:DefinedTerm
    73 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
    74 schema:name Statistics
    75 rdf:type schema:DefinedTerm
    76 sg:person.015255215731.52 schema:affiliation https://www.grid.ac/institutes/grid.462261.5
    77 schema:familyName Grandvalet
    78 schema:givenName Yves
    79 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015255215731.52
    80 rdf:type schema:Person
    81 sg:person.016650156731.69 schema:affiliation https://www.grid.ac/institutes/grid.462261.5
    82 schema:familyName Ambroise
    83 schema:givenName Christophe
    84 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016650156731.69
    85 rdf:type schema:Person
    86 sg:person.0643253341.50 schema:affiliation https://www.grid.ac/institutes/grid.462751.3
    87 schema:familyName d’Alché-Buc
    88 schema:givenName Florence
    89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643253341.50
    90 rdf:type schema:Person
    91 sg:pub.10.1023/a:1007618119488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003090683
    92 https://doi.org/10.1023/a:1007618119488
    93 rdf:type schema:CreativeWork
    94 https://doi.org/10.1006/jcss.1997.1504 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004338842
    95 rdf:type schema:CreativeWork
    96 https://doi.org/10.1214/aos/1016218223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020629296
    97 rdf:type schema:CreativeWork
    98 https://doi.org/10.1214/aos/1024691352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035391848
    99 rdf:type schema:CreativeWork
    100 https://www.grid.ac/institutes/grid.462261.5 schema:alternateName Heuristics and Diagnostics for Complex Systems
    101 schema:name Heudiasyc, UMR CNRS 6599, Université de Technologie de Compiègne, BP 20.529, 60205 Compiègne cedex, France
    102 rdf:type schema:Organization
    103 https://www.grid.ac/institutes/grid.462751.3 schema:alternateName Laboratoire d'informatique de Paris 6
    104 schema:name LIP6, UMR CNRS 7606, Université Pierre et Marie Curie, 4, place Jussieu, 75252 Paris Cedex, France
    105 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...